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Offloading approach for mobile edge computing based on chaotic quantum particle swarm optimization strategy

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Abstract

With the development of 5 g, computing-intensive and complex applications in smart-city is growing rapidly. Due to the limited resources of mobile terminal devices in smart-city, new applications have higher requirements for delay, bandwidth, security, and energy consumption. Computation offloading in mobile edge computing (MEC) is effective to reduce delay and energy consumption of Real-time video analysis. An improved chaos quantum-behaved particle swarm optimization (ICQPSO) algorithm is proposed for multi-user and multi-MEC edge Computation offloading scenarios. Compared with other heuristic algorithms, the improved chaos quantum-behaved particle swarm optimization algorithm can effectively reduce the delay and energy consumption of edge computing offloading. Experimental results show that the improved chaotic quantum-behaved particle swarm optimization (ICQPSO) can effectively avoid premature convergence, has stronger global searchability, and can solve multi-dimensional complex NP-hard problems more efficiently.

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Authors and Affiliations

  1. Tianjin Key Lab of Intelligent Computing and Novel Software Technology, Tianjin University of Technology, Tianjin, 300384, China

    DeGan Zhang, GuiXiang Sun & Peng Yang

  2. School of Sports Economics and Management, Tianjin University of Sport, Tianjin, 301617, China

    Ting Zhang

  3. School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, 100093, China

    Jie Zhang

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  1. DeGan Zhang
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  2. GuiXiang Sun
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  3. Jie Zhang
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  4. Ting Zhang
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  5. Peng Yang
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Zhang, D., Sun, G., Zhang, J. et al. Offloading approach for mobile edge computing based on chaotic quantum particle swarm optimization strategy. J Ambient Intell Human Comput 14, 14333–14347 (2023). https://doi.org/10.1007/s12652-023-04672-z

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